Local interpretability using autoencoder

Abstract

A facility predicts a weight of two or more independent variables used by a subject model trained to predict outcomes using a first dataset which includes values for the independent variables. The facility creates a second dataset by adding noise to the first dataset and trains an autoencoder to reconstruct the first dataset based on the second dataset. The facility access a subject instance, including an output of the subject model, and generates test data based on the subject instance. The facility obtains output from the subject model for each of the data points in the test data. The facility constructs a training observation from the test data and the subject model output, and determines a weight for each training observation by using the autoencoder. The facility trains a local interpretable model based on the determined weight for each training observation.

1 . A method in a computing system for predicting a weight of two or more independent variables used by a subject model, the method comprising:
 accessing a subject model, the subject model having been trained to predict outcomes using a first dataset, the first dataset including the values for the two or more independent variables;   adding noise to the first dataset to create a second dataset;   training an autoencoder to reconstruct the first dataset based on the second dataset;   accessing a subject instance, the subject instance including an output of the subject model;   generating test data, the test data being generated based on the subject instance, the test data including a plurality of data points;   for each of the data points of the test data:
 applying the subject model to the data point to obtain a model output of the value; 
 constructing a training observation comprising the data point and the obtained model output value; and 
 determining a weight for the constructed training observation using the trained autoencoder; and 
   using the constructed training observations and their weights to train a local interpretable model.   
     
     
         2 . The method of  claim 1 , further comprising:
 for each of the data points of the test data:
 computing the Euclidean distance between the training observation and the subject instance within the latent vector space of the trained autoencoder; and 
 determining the weight for the constructed training observation based on the computed Euclidean distance between the training observation and the subject instance. 
   
     
     
         3 . The method of  claim 1 , further comprising:
 accessing the coefficients of the local interpretable model; and   identifying the influence of each of the independent variables on the subject instance for the subject model based on the coefficients of the local interpretable model.   
     
     
         4 . The method of  claim 1 , wherein the local interpretable model is a linear regression model. 
     
     
         5 . The method of  claim 1 , wherein the local interpretable model is one or more decision trees. 
     
     
         6 . One or more memories collectively storing a local interpretable model training data structure, the data structure comprising:
 information indicating a first dataset, the first dataset including the values for two or more independent variables;   information indicating a subject model, the subject model having been trained to predict outcomes using the first dataset;   information indicating a second dataset, the second dataset having been created by adding noise to the first dataset;   information indicating an autoencoder, the autoencoder having been trained to reconstruct the first dataset from the second dataset;   information indicating a subject instance, the subject instance including an output of the subject model;   information indicating test data, the test data being generated based on the subject instance, the test data including a plurality of data points; and   information indicating subject model output for each of the data points of the test data, the subject model output having been obtained by applying the subject model to each of the data points of the test data,   
       such that, the subject model output is usable to create training observations, each training observation comprising the data point and the subject model output for the data point, 
       and such that the autoencoder is usable to create a weighted data point based on the training observations and train a linear interpretable model based on the training observations. 
     
     
         7 . The one or more memories of  claim 6 , the data structure further comprising:
 a plurality of Euclidean distances for each data point, the Euclidean distance being determined by measuring the distance between the training observation and the subject instance within the latent vector space of the trained autoencoder,   
       such that the plurality of Euclidean distances are usable to create the weighted data point based on the training observations. 
     
     
         8 . The one or more memories of  claim 6 , the data structure further comprising:
 a coefficient of each of the independent variables of the local interpretable model,   
       such that the coefficient of each of the independent variables of the local interpretable model are usable to identify the influence of each of the independent variables on the subject instance. 
     
     
         9 . The one or more memories of  claim 6 , wherein the local interpretable model is a linear regression model. 
     
     
         10 . The one or more memories of  claim 6 , wherein the local interpretable model is one or more decision trees. 
     
     
         11 . A system for predicting a weight of two or more independent variables used by a subject model, the system comprising:
 a computing device having access to a subject model, the computing device additionally having access to a first dataset, the subject model having been trained to predict outcomes using the first dataset, the first dataset including the values for two or more independent variables; and   the computing device being configured to:
 add noise to the first dataset to create a second dataset; 
 train an autoencoder to reconstruct the first dataset based on the second dataset; 
 access a subject instance, the subject instance including an output of the subject model; 
 generate test data, the test data being generated based on the subject instance, the test data including a plurality of data points; 
 for each of the data points of the test data:
 apply the subject model to the data point to obtain a model output of the value; 
 construct a training observation comprising the data point and the obtained model output value; and 
 determine a weight for the constructed training observation using the trained autoencoder; and 
 use the constructed training observations and their weights to train a local interpretable model. 
 
   
     
     
         12 . The system of  claim 11 , wherein the computing device is further configured to:
 for each of the data points of the test data:
 compute the Euclidean distance between the training observation and the subject instance within the latent vector space of the trained autoencoder; and 
 determine the weight for the constructed training observation based on the computed Euclidean distance between the training observation and the subject instance. 
   
     
     
         13 . The system of  claim 11 , wherein the computing device is further configured to:
 access the coefficients of the local interpretable model; and   identify the influence of each of the independent variables on the subject instance for the subject model based on the coefficients of the local interpretable model.   
     
     
         14 . The system of  claim 11 , wherein the local interpretable model is a linear regression model. 
     
     
         15 . The system of  claim 11 , wherein the local interpretable model is one or more decision trees.